A learning‐based material decomposition pipeline for multi‐energy x‐ray imaging

Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Xia, Yan; Choi, Jang-Hwan; Chen, Shuqing; Hu, Shicheng; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas

doi:10.1002/mp.13317

Cited by 28 publications

(26 citation statements)

References 31 publications

(54 reference statements)

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“…Lu et al [64] conducted a feasibility study for material decomposition using a CNN model. The performance was quantitatively assessed using a simulated extended cardiactorso phantom and an anthropomorphic torso phantom.…”

Section: Quality Assurancementioning

confidence: 99%

Deep Learning in Radiation Oncology

Cheon

Kim

2020

Progress in Medical Physics

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Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

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Section: Quality Assurancementioning

confidence: 99%

Deep Learning in Radiation Oncology

Cheon

Kim

2020

Progress in Medical Physics

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“…Clark et al adapted a well‐known CNN (U‐net 18 ) to decompose micro‐CT images into material maps 19 . Lu et al 20 used classical machine learning algorithms (e.g., ANN and random forest) to decompose multi‐energy projection data into material‐specific projections (iodine/bone or biopsy needle/bone); they also adapted two well‐known CNNs (DnCNN 21 and ResNet 22 ). Zhang et al 23 developed a butterfly‐net that imitated a linear system model based on two basis materials (bone and soft‐tissue).…”

Section: Introductionmentioning

confidence: 99%

“…[19]); the DnCNN and the ResNet used in Ref. [20] yielded a mean structural similarity index ≤0.77 for the decomposed iodine images (see Table III in Ref. [20]).…”

Section: Introductionmentioning

confidence: 99%

“…Second, the networks in Refs. [20,23] were trained using images with anatomical background; the material labeling procedure performed during training is challenging; it is susceptible to registration/quantification errors that propagate to the material-specific images. For example, to generate training labels for butterfly-net, 23 a commercial bone-removal software (Syngo.Via, Siemens Healthcare, Inc., Forchheim, Germany) was used to provide initial binary material separation, and then radiologists were recruited to empirically correct pixelwise artifact, for example, the bone residual in soft-tissue images and the blurred boundaries.…”

Section: Introductionmentioning

confidence: 99%

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Deep‐learning‐based direct inversion for material decomposition

et al. 2020

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Purpose To develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi‐energy computed tomography (CT) images without performing conventional material decomposition. Methods The proposed CNN (denoted as Incept‐net) followed the general framework of encoder–decoder network, with an assumption that local image information was sufficient for modeling the nonlinear physical process of multi‐energy CT. Incept‐net was implemented with a customized loss function, including an in‐house‐designed image‐gradient‐correlation (IGC) regularizer to improve edge preservation. The network consisted of two types of customized multibranch modules exploiting multiscale feature representation to improve the robustness over local image noise and artifacts. Inserts with various densities of different materials [hydroxyapatite (HA), iodine, a blood–iodine mixture, and fat] were scanned using a research photon‐counting detector (PCD) CT with two energy thresholds and multiple radiation dose levels. The network was trained using phantom image patches only, and tested with different‐configurations of full field‐of‐view phantom and in vivo porcine images. Furthermore, the nominal mass densities of insert materials were used as the labels in CNN training, which potentially provided an implicit mass conservation constraint. The Incept‐net performance was evaluated in terms of image noise, detail preservation, and quantitative accuracy. Its performance was also compared to common material decomposition algorithms including least‐square‐based material decomposition (LS‐MD), total‐variation regularized material decomposition (TV‐MD), and U‐net‐based method. Results Incept‐net improved accuracy of the predicted mass density of basis materials compared with the U‐net, TV‐MD, and LS‐MD: the mean absolute error (MAE) of iodine was 0.66, 1.0, 1.33, and 1.57 mgI/cc for Incept‐net, U‐net, TV‐MD, and LS‐MD, respectively, across all iodine‐present inserts (2.0–24.0 mgI/cc). With the LS‐MD as the baseline, Incept‐net and U‐net achieved comparable noise reduction (both around 95%), both higher than TV‐MD (85%). The proposed IGC regularizer effectively helped both Incept‐net and U‐net to reduce image artifact. Incept‐net closely conserved the total mass densities (i.e., mass conservation constraint) in porcine images, which heuristically validated the quantitative accuracy of its outputs in anatomical background. In general, Incept‐net performance was less dependent on radiation dose levels than the two conventional methods; with approximately 40% less parameters, the Incept‐net achieved relatively improved performance than the comparator U‐net, indicating that performance gain by Incept‐net was not achieved by simply increasing network learning capacity. Conclusion Incept‐net demonstrated superior qualitative image appearance, quantitative accuracy, and lower noise than the conventional methods and less sensitive to dose change. Incept‐net generalized and performed well with unseen image structures and d...

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“…Over the recent years, deep learning has achieved promising and impressive advancements, demonstrating potential capacities to address various computer vision tasks [12], especially in medical images analysis. In the field of ophthalmological image processing, deep learning has been applied in various aspects such as denoising [13], SR [14], reconstruction [15,16], and image synthesis [17].…”

Section: Introductionmentioning

confidence: 99%

N2NSR‐OCT: Simultaneous denoising and super‐resolution in optical coherence tomography images using semisupervised deep learning

Qiu

You

Huang

et al. 2020

Journal of Biophotonics

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Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal-tonoise ratio (SNR) and high-resolution (HR) OCT images within a short scanning time, we presented a learning-based method to recover high-quality OCT images from noisy and low-resolution OCT images. We proposed a semisupervised learning approach named N2NSR-OCT, to generate denoised and super-resolved OCT images simultaneously using up-and down-sampling networks (U-Net (Semi) and DBPN (Semi)). Additionally, two different super-resolution and denoising models with different upscale factors (2× and 4×) were trained to recover the highquality OCT image of the corresponding down-sampling rates. The new semisupervised learning approach is able to achieve results comparable with those of supervised learning using up-and down-sampling networks, and can produce better performance than other related state-of-the-art methods in the aspects of maintaining subtle fine retinal structures.

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A learning‐based material decomposition pipeline for multi‐energy x‐ray imaging

Cited by 28 publications

References 31 publications

Deep Learning in Radiation Oncology

Deep Learning in Radiation Oncology

Deep‐learning‐based direct inversion for material decomposition

N2NSR‐OCT: Simultaneous denoising and super‐resolution in optical coherence tomography images using semisupervised deep learning

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